Can PIPEDA ‘Face’ the Challenge? An Analysis of the Adequacy of Canada’s Private Sector Privacy Legislation against Facial Recognition Technology
Bibliographic record
Abstract
Facial recognition technology is one of the most intrusive and privacy threatening technologies available today. The literature around this technology mainly focuses on its use by the public sector as a mass surveillance tool; however, the private sector uses of facial recognition technologies also raise significant privacy concerns. This paper aims to identify and examine the privacy implications of the private sector uses of facial recognition technologies and the adequacy of Canada’s federal private sector privacy legislation, the Personal Information Protection and Electronic Documents Act (PIPEDA), in addressing these privacy concerns. Facial templates produced and recorded by these technologies are types of biometric information. While all biometric information are highly sensitive in nature and require stricter regulatory protections, this paper argues that facial recognition data poses a more significant risk to individuals’ privacy and autonomy than other forms of biometric data. First, facial recognition technology can be used on a person from large distances and completely surreptitiously. Second, hiding one’s face or avoiding the technology in daily life is hard to accomplish, if not impossible. Third, there is already a vast database of personally identified facial images available in the hands of private organizations, such as social media companies, containing the sensitive data of millions of people ready to be identified. This paper takes the position that PIPEDA does not provide adequate protection of individuals’ privacy against facial recognition technologies and significant amendments to the Act are urgently required. Sensitive personal information, which includes facial recognition data, should be defined as a special category of personal information along with stricter protections on their collection, use, retention and disclosure. The role of consent should be re-defined concerning highly sensitive facial recognition data, clear enforcement powers should be given to the Privacy Commissioner of Canada in enforcing the Act and mechanisms to ensure compliance should be regulated in the forms of substantial fines for violations and a private right of action for citizens. However, the paper argues that due to PIPEDA’s limited scope of application and constitutional challenges, amending the Act would not be enough to protect all Canadians from the risks this technology poses. Accordingly, the paper concludes that along with the proposed amendments to PIPEDA, provincial legislations should also be enacted and enforced to ensure the protection of Canadians’ privacy and autonomy against the threats posed by facial recognition technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".